1 /*
2 * Copyright (c) 2017-2020 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #ifndef ARM_COMPUTE_TEST_SIMPLE_TENSOR_H
25 #define ARM_COMPUTE_TEST_SIMPLE_TENSOR_H
26
27 #include "arm_compute/core/TensorShape.h"
28 #include "arm_compute/core/Types.h"
29 #include "arm_compute/core/Utils.h"
30 #include "support/MemorySupport.h"
31 #include "tests/IAccessor.h"
32 #include "tests/Utils.h"
33
34 #include <algorithm>
35 #include <array>
36 #include <cstddef>
37 #include <cstdint>
38 #include <functional>
39 #include <memory>
40 #include <stdexcept>
41 #include <utility>
42
43 namespace arm_compute
44 {
45 namespace test
46 {
47 class RawTensor;
48
49 /** Simple tensor object that stores elements in a consecutive chunk of memory.
50 *
51 * It can be created by either loading an image from a file which also
52 * initialises the content of the tensor or by explcitly specifying the size.
53 * The latter leaves the content uninitialised.
54 *
55 * Furthermore, the class provides methods to convert the tensor's values into
56 * different image format.
57 */
58 template <typename T>
59 class SimpleTensor : public IAccessor
60 {
61 public:
62 /** Create an uninitialised tensor. */
63 SimpleTensor() = default;
64
65 /** Create an uninitialised tensor of the given @p shape and @p format.
66 *
67 * @param[in] shape Shape of the new raw tensor.
68 * @param[in] format Format of the new raw tensor.
69 */
70 SimpleTensor(TensorShape shape, Format format);
71
72 /** Create an uninitialised tensor of the given @p shape and @p data type.
73 *
74 * @param[in] shape Shape of the new raw tensor.
75 * @param[in] data_type Data type of the new raw tensor.
76 * @param[in] num_channels (Optional) Number of channels (default = 1).
77 * @param[in] quantization_info (Optional) Quantization info for asymmetric quantization (default = empty).
78 * @param[in] data_layout (Optional) Data layout of the tensor (default = NCHW).
79 */
80 SimpleTensor(TensorShape shape, DataType data_type,
81 int num_channels = 1,
82 QuantizationInfo quantization_info = QuantizationInfo(),
83 DataLayout data_layout = DataLayout::NCHW);
84
85 /** Create a deep copy of the given @p tensor.
86 *
87 * @param[in] tensor To be copied tensor.
88 */
89 SimpleTensor(const SimpleTensor &tensor);
90
91 /** Create a deep copy of the given @p tensor.
92 *
93 * @param[in] tensor To be copied tensor.
94 *
95 * @return a copy of the given tensor.
96 */
97 SimpleTensor &operator=(SimpleTensor tensor);
98 /** Allow instances of this class to be move constructed */
99 SimpleTensor(SimpleTensor &&) = default;
100 /** Default destructor. */
101 ~SimpleTensor() = default;
102
103 /** Tensor value type */
104 using value_type = T;
105 /** Tensor buffer pointer type */
106 using Buffer = std::unique_ptr<value_type[]>;
107
108 friend class RawTensor;
109
110 /** Return value at @p offset in the buffer.
111 *
112 * @param[in] offset Offset within the buffer.
113 *
114 * @return value in the buffer.
115 */
116 T &operator[](size_t offset);
117
118 /** Return constant value at @p offset in the buffer.
119 *
120 * @param[in] offset Offset within the buffer.
121 *
122 * @return constant value in the buffer.
123 */
124 const T &operator[](size_t offset) const;
125
126 /** Shape of the tensor.
127 *
128 * @return the shape of the tensor.
129 */
130 TensorShape shape() const override;
131 /** Size of each element in the tensor in bytes.
132 *
133 * @return the size of each element in the tensor in bytes.
134 */
135 size_t element_size() const override;
136 /** Total size of the tensor in bytes.
137 *
138 * @return the total size of the tensor in bytes.
139 */
140 size_t size() const override;
141 /** Image format of the tensor.
142 *
143 * @return the format of the tensor.
144 */
145 Format format() const override;
146 /** Data layout of the tensor.
147 *
148 * @return the data layout of the tensor.
149 */
150 DataLayout data_layout() const override;
151 /** Data type of the tensor.
152 *
153 * @return the data type of the tensor.
154 */
155 DataType data_type() const override;
156 /** Number of channels of the tensor.
157 *
158 * @return the number of channels of the tensor.
159 */
160 int num_channels() const override;
161 /** Number of elements of the tensor.
162 *
163 * @return the number of elements of the tensor.
164 */
165 int num_elements() const override;
166 /** Available padding around the tensor.
167 *
168 * @return the available padding around the tensor.
169 */
170 PaddingSize padding() const override;
171 /** Quantization info in case of asymmetric quantized type
172 *
173 * @return
174 */
175 QuantizationInfo quantization_info() const override;
176
177 /** Constant pointer to the underlying buffer.
178 *
179 * @return a constant pointer to the data.
180 */
181 const T *data() const;
182
183 /** Pointer to the underlying buffer.
184 *
185 * @return a pointer to the data.
186 */
187 T *data();
188
189 /** Read only access to the specified element.
190 *
191 * @param[in] coord Coordinates of the desired element.
192 *
193 * @return A pointer to the desired element.
194 */
195 const void *operator()(const Coordinates &coord) const override;
196
197 /** Access to the specified element.
198 *
199 * @param[in] coord Coordinates of the desired element.
200 *
201 * @return A pointer to the desired element.
202 */
203 void *operator()(const Coordinates &coord) override;
204
205 /** Swaps the content of the provided tensors.
206 *
207 * @param[in, out] tensor1 Tensor to be swapped.
208 * @param[in, out] tensor2 Tensor to be swapped.
209 */
210 template <typename U>
211 friend void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2);
212
213 protected:
214 Buffer _buffer{ nullptr };
215 TensorShape _shape{};
216 Format _format{ Format::UNKNOWN };
217 DataType _data_type{ DataType::UNKNOWN };
218 int _num_channels{ 0 };
219 QuantizationInfo _quantization_info{};
220 DataLayout _data_layout{ DataLayout::UNKNOWN };
221 };
222
223 template <typename T1, typename T2>
copy_tensor(const SimpleTensor<T2> & tensor)224 SimpleTensor<T1> copy_tensor(const SimpleTensor<T2> &tensor)
225 {
226 SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
227 tensor.num_channels(),
228 tensor.quantization_info(),
229 tensor.data_layout());
230 for(size_t n = 0; n < size_t(st.num_elements()); n++)
231 {
232 st.data()[n] = static_cast<T1>(tensor.data()[n]);
233 }
234 return st;
235 }
236
237 template <typename T1, typename T2, typename std::enable_if<std::is_same<T1, T2>::value, int>::type = 0>
copy_tensor(const SimpleTensor<half> & tensor)238 SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor)
239 {
240 SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
241 tensor.num_channels(),
242 tensor.quantization_info(),
243 tensor.data_layout());
244 memcpy((void *)st.data(), (const void *)tensor.data(), size_t(st.num_elements() * sizeof(T1)));
245 return st;
246 }
247
248 template < typename T1, typename T2, typename std::enable_if < (std::is_same<T1, half>::value || std::is_same<T2, half>::value), int >::type = 0 >
copy_tensor(const SimpleTensor<half> & tensor)249 SimpleTensor<T1> copy_tensor(const SimpleTensor<half> &tensor)
250 {
251 SimpleTensor<T1> st(tensor.shape(), tensor.data_type(),
252 tensor.num_channels(),
253 tensor.quantization_info(),
254 tensor.data_layout());
255 for(size_t n = 0; n < size_t(st.num_elements()); n++)
256 {
257 st.data()[n] = half_float::detail::half_cast<T1, T2>(tensor.data()[n]);
258 }
259 return st;
260 }
261
262 template <typename T>
SimpleTensor(TensorShape shape,Format format)263 SimpleTensor<T>::SimpleTensor(TensorShape shape, Format format)
264 : _buffer(nullptr),
265 _shape(shape),
266 _format(format),
267 _quantization_info(),
268 _data_layout(DataLayout::NCHW)
269 {
270 _num_channels = num_channels();
271 _buffer = support::cpp14::make_unique<T[]>(num_elements() * _num_channels);
272 }
273
274 template <typename T>
SimpleTensor(TensorShape shape,DataType data_type,int num_channels,QuantizationInfo quantization_info,DataLayout data_layout)275 SimpleTensor<T>::SimpleTensor(TensorShape shape, DataType data_type, int num_channels, QuantizationInfo quantization_info, DataLayout data_layout)
276 : _buffer(nullptr),
277 _shape(shape),
278 _data_type(data_type),
279 _num_channels(num_channels),
280 _quantization_info(quantization_info),
281 _data_layout(data_layout)
282 {
283 _buffer = support::cpp14::make_unique<T[]>(this->_shape.total_size() * _num_channels);
284 }
285
286 template <typename T>
SimpleTensor(const SimpleTensor & tensor)287 SimpleTensor<T>::SimpleTensor(const SimpleTensor &tensor)
288 : _buffer(nullptr),
289 _shape(tensor.shape()),
290 _format(tensor.format()),
291 _data_type(tensor.data_type()),
292 _num_channels(tensor.num_channels()),
293 _quantization_info(tensor.quantization_info()),
294 _data_layout(tensor.data_layout())
295 {
296 _buffer = support::cpp14::make_unique<T[]>(tensor.num_elements() * _num_channels);
297 std::copy_n(tensor.data(), this->_shape.total_size() * _num_channels, _buffer.get());
298 }
299
300 template <typename T>
301 SimpleTensor<T> &SimpleTensor<T>::operator=(SimpleTensor tensor)
302 {
303 swap(*this, tensor);
304
305 return *this;
306 }
307
308 template <typename T>
309 T &SimpleTensor<T>::operator[](size_t offset)
310 {
311 return _buffer[offset];
312 }
313
314 template <typename T>
315 const T &SimpleTensor<T>::operator[](size_t offset) const
316 {
317 return _buffer[offset];
318 }
319
320 template <typename T>
shape()321 TensorShape SimpleTensor<T>::shape() const
322 {
323 return _shape;
324 }
325
326 template <typename T>
element_size()327 size_t SimpleTensor<T>::element_size() const
328 {
329 return num_channels() * element_size_from_data_type(data_type());
330 }
331
332 template <typename T>
quantization_info()333 QuantizationInfo SimpleTensor<T>::quantization_info() const
334 {
335 return _quantization_info;
336 }
337
338 template <typename T>
size()339 size_t SimpleTensor<T>::size() const
340 {
341 const size_t size = std::accumulate(_shape.cbegin(), _shape.cend(), 1, std::multiplies<size_t>());
342 return size * element_size();
343 }
344
345 template <typename T>
format()346 Format SimpleTensor<T>::format() const
347 {
348 return _format;
349 }
350
351 template <typename T>
data_layout()352 DataLayout SimpleTensor<T>::data_layout() const
353 {
354 return _data_layout;
355 }
356
357 template <typename T>
data_type()358 DataType SimpleTensor<T>::data_type() const
359 {
360 if(_format != Format::UNKNOWN)
361 {
362 return data_type_from_format(_format);
363 }
364 else
365 {
366 return _data_type;
367 }
368 }
369
370 template <typename T>
num_channels()371 int SimpleTensor<T>::num_channels() const
372 {
373 switch(_format)
374 {
375 case Format::U8:
376 case Format::U16:
377 case Format::S16:
378 case Format::U32:
379 case Format::S32:
380 case Format::F16:
381 case Format::F32:
382 return 1;
383 // Because the U and V channels are subsampled
384 // these formats appear like having only 2 channels:
385 case Format::YUYV422:
386 case Format::UYVY422:
387 return 2;
388 case Format::UV88:
389 return 2;
390 case Format::RGB888:
391 return 3;
392 case Format::RGBA8888:
393 return 4;
394 case Format::UNKNOWN:
395 return _num_channels;
396 //Doesn't make sense for planar formats:
397 case Format::NV12:
398 case Format::NV21:
399 case Format::IYUV:
400 case Format::YUV444:
401 default:
402 return 0;
403 }
404 }
405
406 template <typename T>
num_elements()407 int SimpleTensor<T>::num_elements() const
408 {
409 return _shape.total_size();
410 }
411
412 template <typename T>
padding()413 PaddingSize SimpleTensor<T>::padding() const
414 {
415 return PaddingSize(0);
416 }
417
418 template <typename T>
data()419 const T *SimpleTensor<T>::data() const
420 {
421 return _buffer.get();
422 }
423
424 template <typename T>
data()425 T *SimpleTensor<T>::data()
426 {
427 return _buffer.get();
428 }
429
430 template <typename T>
operator()431 const void *SimpleTensor<T>::operator()(const Coordinates &coord) const
432 {
433 return _buffer.get() + coord2index(_shape, coord) * _num_channels;
434 }
435
436 template <typename T>
operator()437 void *SimpleTensor<T>::operator()(const Coordinates &coord)
438 {
439 return _buffer.get() + coord2index(_shape, coord) * _num_channels;
440 }
441
442 template <typename U>
swap(SimpleTensor<U> & tensor1,SimpleTensor<U> & tensor2)443 void swap(SimpleTensor<U> &tensor1, SimpleTensor<U> &tensor2)
444 {
445 // Use unqualified call to swap to enable ADL. But make std::swap available
446 // as backup.
447 using std::swap;
448 swap(tensor1._shape, tensor2._shape);
449 swap(tensor1._format, tensor2._format);
450 swap(tensor1._data_type, tensor2._data_type);
451 swap(tensor1._num_channels, tensor2._num_channels);
452 swap(tensor1._quantization_info, tensor2._quantization_info);
453 swap(tensor1._buffer, tensor2._buffer);
454 }
455 } // namespace test
456 } // namespace arm_compute
457 #endif /* ARM_COMPUTE_TEST_SIMPLE_TENSOR_H */
458